"We are looking at how can we can optimise the placement of the turbines on the wind farm," says Neumann.

He says that turbines on a wind farm can block the flow of wind to other turbines, depending on where they are placed.

So-called "wake effects" can significantly reduce the amount of energy generated by the wind farm and there needs to be trade-off between saving space and reducing wake effects.

Neumann and colleagues have developed computer algorithms that can minimise these effects and maximise the energy generated, given a particular terrain, turbine type, wind direction and area of land.

They are called "evolutionary algorithms" because they are inspired by biological evolution, says Neumann.

The process begins with a particular set of turbine layouts, which can be thought of as "parent" solutions to the problem. Each parent is the basis of a new set of layouts each with different characteristics, which can be thought of as "offspring" solutions.

The idea is to replicate what happens in evolution where each generation is supposed to improve on the one before.

"In the end you pick the best solution from your set," says Neumann.

He says using his algorithms it's possible to increase the energy output of a wind farm by four to five per cent.

"On the profit side this would go into hundreds of millions [of dollars] over one year for a large wind farm," says Neumann.

Supercomputer power

Neumann says it takes an enormous amount of computer power to evaluate the interaction between all the different turbines in different scenarios.

He says other approaches to optimising layout of wind farms can only deal with a small number of turbines, but his algorithm can model up to 1000.

Neumann says to keep computation time down to a week, a supercomputer is required to evaluate all the solutions in parallel.

"It could take a year on a normal computer," he says.

Neumann says other biology-inspired algorithms being used to inspire solutions to complex problems are based on ant colonies.

Ants are very good at finding the shortest way to a food source from their nest by communicating with each other and using pheromone trails, he says.

Neuman is using the same principles to develop algorithms that can, for example, solve the shortest return route that takes in several cities.